---
title: "Rethinking SQL ETL for modern data platforms"
description: "SQL ETL is often distributed across a warehouse, transformation framework, orchestrator, monitoring, lineage, and data-quality systems, creating operational fragmentation as data teams scale. The post proposes a unified platform model that combines execution, orchestration, observability, governance, and optimization while supporting analytics engineers, warehouse engineers, and analysts through dbt, scripts and stored procedures, Materialized Views, declarative pipelines, and no-code tools. Databricks implements this model with shared execution, governance, and observability, using serverless infrastructure and AI-driven optimization alongside open table formats and ANSI SQL. The stated benefits include reduced operational overhead, support for batch and real-time workloads, and incremental modernization rather than disruptive rewrites. Reported examples include HP’s 32% cloud savings and 36% lower combined job runtime after moving to serverless compute, and Adobe reducing query execution from eight minutes to three seconds with Materialized Views."
---

# Rethinking SQL ETL for modern data platforms

[Databricks](https://yomu.fyi/company/databricks) · Matt Jones, Shanelle Roman · Apr 29, 2026

**Type:** Problem & solution

## Summary

SQL ETL is often distributed across a warehouse, transformation framework, orchestrator, monitoring, lineage, and data-quality systems, creating operational fragmentation as data teams scale. The post proposes a unified platform model that combines execution, orchestration, observability, governance, and optimization while supporting analytics engineers, warehouse engineers, and analysts through dbt, scripts and stored procedures, Materialized Views, declarative pipelines, and no-code tools. Databricks implements this model with shared execution, governance, and observability, using serverless infrastructure and AI-driven optimization alongside open table formats and ANSI SQL. The stated benefits include reduced operational overhead, support for batch and real-time workloads, and incremental modernization rather than disruptive rewrites. Reported examples include HP’s 32% cloud savings and 36% lower combined job runtime after moving to serverless compute, and Adobe reducing query execution from eight minutes to three seconds with Materialized Views.

## Context

SQL ETL commonly spans separate systems for warehouse execution, transformation, orchestration, monitoring, lineage, and data quality. This fragmentation makes dependencies and failures harder to trace, increases operational overhead, and can force organizations to add systems or rewrite pipelines as workloads evolve.

## Approach / What changed

Use Databricks as a unified SQL ETL platform combining execution, orchestration, observability, governance, and optimization. The platform supports dbt workflows, SQL scripts and stored procedures, Materialized Views, declarative pipelines, and no-code preparation through a shared execution engine, governance model, and observability framework, with open table formats, ANSI SQL, and incremental modernization.

## Takeaways

- Databricks supports multiple SQL pipeline authoring styles, including dbt, stored procedures, Materialized Views, declarative pipelines, and no-code data preparation, without separating their execution and governance foundations.
- The proposed architecture uses open table formats and ANSI SQL, while applying one SQL model to batch and real-time analytics workloads to reduce portability and lock-in concerns.
- Reported results include HP’s 32% cloud savings and 36% reduction in combined job runtime after moving pipelines to serverless compute, plus Adobe reducing Materialized View query execution from eight minutes to three seconds.

**Tags:** [Databricks](https://yomu.fyi/topic/databricks), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Lakehouse](https://yomu.fyi/topic/lakehouse), [SQL](https://yomu.fyi/topic/sql)

- Source: [Databricks](https://www.databricks.com/blog/rethinking-sql-etl-modern-data-platforms)
- Source URL: https://www.databricks.com/blog/rethinking-sql-etl-modern-data-platforms
- Ingested by Yomu: 2026-08-31T03:40:33.777Z

[Read original post](https://www.databricks.com/blog/rethinking-sql-etl-modern-data-platforms)